graph LR
A(Data Collection)
B(Data Ingestion)
C(Data Preparation )
D(Data Aggregation)
E(Data Computation)
F(Descriptive Analysis)
G(Inferential Analysis)
H(Geographical Analysis)
A --> B
B --> C
C --> D
D --> E
E --> F
E --> G
E --> H
style A fill:#f9d5e5,stroke:#eeac99
style B fill:#cce5ff,stroke:#99c2ff
style C fill:#d4f4dd,stroke:#a3d9b1
style D fill:#fff3cd,stroke:#ffeb99
style E fill:#e2cbf7,stroke:#c299ff
style F fill:#cce5ff,stroke:#99c2ff
style G fill:#cce5ff,stroke:#99c2ff
style H fill:#cce5ff,stroke:#99c2ff
Weekly and Annual Earnings (2011-2023)
Overview
In this analysis, I am exploring trends and patterns in earnings data, focusing on both weekly and annual earnings across various sectors, age groups, and years. The data includes multiple datasets on earning estimates, percentiles, and geographical distributions. I started by processing and cleaning the data, selecting relevant columns and filtering out unwanted metrics. Then, I aggregated the earnings data by sector, year, and age group to provide a clear picture of the trends in mean weekly and annual earnings over time. I also performed descriptive statistics to understand the distribution and variation in earnings. The analysis includes an interactive visualization of earnings trends and a heatmap of earnings percentiles, helping to understand how earnings vary over time and across different percentiles. Additionally, I used generalized additive models (GAM) and generalized linear models (GLM) to predict annual earnings based on weekly earnings, age group, and sector. Finally, I conducted a geographical analysis, mapping total earnings across counties in 2011 and 2021. The overarching question I am trying to answer is: How do earnings trends vary across sectors, age groups, and regions, and what factors most influence these trends?
Process Flow
Table Relationships
erDiagram
weekly_earning_estimates {
string Sector
int Calender_Year
string Age_Group
float Euros
}
annual_earning_estimates {
string Sector
int Calender_Year
string Age_Group
float Euros
}
weekly_earning_percentiles {
int Calender_Year
string Percentile
string Age_Group
float Euros
}
annual_earning_percentiles {
int Calender_Year
string Percentile
string Age_Group
float Euros
}
weekly_earning_estimates ||--o| annual_earning_estimates : "Sector, Calender_Year, Age_Group"
weekly_earning_percentiles ||--o| annual_earning_percentiles : "Calender_Year, Percentile, Age_Group"
weekly_earning_estimates ||--o| weekly_earning_percentiles : "Calender_Year, Age_Group"
annual_earning_estimates ||--o| annual_earning_percentiles : "Calender_Year, Age_Group"
Data Collection
This data is sourced from Click here
Data Ingestion
library(leaflet)
library(sf)Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUElibrary(readr)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(ggplot2)
library(mgcv) Loading required package: nlme
Attaching package: 'nlme'The following object is masked from 'package:dplyr':
collapseThis is mgcv 1.9-1. For overview type 'help("mgcv-package")'.library(psych)
Attaching package: 'psych'The following objects are masked from 'package:ggplot2':
%+%, alphalibrary(plotly)
Attaching package: 'plotly'The following object is masked from 'package:ggplot2':
last_plotThe following object is masked from 'package:stats':
filterThe following object is masked from 'package:graphics':
layoutlibrary(htmltools)
library(RColorBrewer)
data_1 <- read_csv("DDA02.20250317T140346.csv")Rows: 30576 Columns: 12── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): STATISTIC, Statistic Label, C02199V02655, Sex, C02665V03225, NACE R...
dbl (4): TLIST(A1), Year, C02076V02508, VALUE
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.data_2 <- read_csv("DDA11.20250317T170356.csv")Rows: 6916 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): Statistic Label, Percentile, Age Group, UNIT
dbl (2): Year, VALUE
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.data_3<- read_csv("DEA08.20250323T180312.csv")Rows: 9828 Columns: 10
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (7): STATISTIC, Statistic Label, C03788V04538, County, C02665V03225, NAC...
dbl (3): TLIST(A1), Year, VALUE
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Data Preparation
In this step we process the data into a form factor more suitable for our analysis
Dataset 1
Selecting Relevant Columns
earning_estimates <- data_1 %>%
select(`Statistic Label`, Year, Sex, `NACE Rev 2 Sector`,`Age Group`,VALUE )Renaming Columns
earning_estimates <- earning_estimates %>% rename( Metrics = `Statistic Label`,
Calender_Year = Year,
Sector = `NACE Rev 2 Sector`,
Age_Group =`Age Group`,
Euros = VALUE)Filtering Columns
earning_estimates <- earning_estimates %>% filter(!Metrics %in% c("Annual Change (Mean Weekly Earnings)",
"Annual Change (Median Weekly Earnings)",
"Annual Change (Mean Annual Earnings)",
"Annual Change (Median Annual Earnings)",
"Median Weekly Earnings",
"Median Annual Earnings"
))
earning_estimates <- earning_estimates %>% filter(!Sex %in% c("Both sexes"))Dataset 2
Renaming Columns
earning_percentiles <- data_2 %>% rename( Metrics = `Statistic Label`,
Calender_Year = Year,
Age_Group =`Age Group`,
Euros = VALUE)Filtering Columns
earning_percentiles <- earning_percentiles %>% filter(!Metrics %in% c("Annual Change (Weekly Earnings)",
"Annual Change (Annual Earnings)"))Dataset 3
Selecting Relevant Columns
geo_distrbution <- data_3 %>%
select(Year,County,`NACE Rev 2 Economic Sector`,VALUE )Rename Columns
geo_distrbution <- geo_distrbution %>% rename(Sector = `NACE Rev 2 Economic Sector`,
Euros = VALUE) Data Aggregation
Aggregated Weekly Earning Estimates
weekly_earning_estimates <- earning_estimates %>%
filter(Metrics == "Mean Weekly Earnings")
agg_weekly_earning_estimates <- weekly_earning_estimates %>% group_by(Sector, Calender_Year, Age_Group) %>%
summarise(
Total_Euros = sum(Euros, na.rm = TRUE))`summarise()` has grouped output by 'Sector', 'Calender_Year'. You can override
using the `.groups` argument.print(agg_weekly_earning_estimates)# A tibble: 1,274 × 4
# Groups: Sector, Calender_Year [182]
Sector Calender_Year Age_Group Total_Euros
<chr> <dbl> <chr> <dbl>
1 Accommodation and food service activitie… 2011 15 - 24 … 420.
2 Accommodation and food service activitie… 2011 15 years… 642.
3 Accommodation and food service activitie… 2011 25 - 29 … 658.
4 Accommodation and food service activitie… 2011 30 - 39 … 769.
5 Accommodation and food service activitie… 2011 40 - 49 … 806.
6 Accommodation and food service activitie… 2011 50 - 59 … 739.
7 Accommodation and food service activitie… 2011 60 years… 647.
8 Accommodation and food service activitie… 2012 15 - 24 … 418.
9 Accommodation and food service activitie… 2012 15 years… 639.
10 Accommodation and food service activitie… 2012 25 - 29 … 653.
# ℹ 1,264 more rowsAggregated Annual Earning Estimates
annual_earning_estimates <- earning_estimates %>%
filter(Metrics == "Mean Annual Earnings")
agg_annual_earning_estimates <- annual_earning_estimates %>% group_by(Sector, Calender_Year, Age_Group) %>%
summarise(
Total_Euros = sum(Euros, na.rm = TRUE))`summarise()` has grouped output by 'Sector', 'Calender_Year'. You can override
using the `.groups` argument.print(agg_annual_earning_estimates)# A tibble: 1,274 × 4
# Groups: Sector, Calender_Year [182]
Sector Calender_Year Age_Group Total_Euros
<chr> <dbl> <chr> <dbl>
1 Accommodation and food service activitie… 2011 15 - 24 … 26613
2 Accommodation and food service activitie… 2011 15 years… 39865
3 Accommodation and food service activitie… 2011 25 - 29 … 37642
4 Accommodation and food service activitie… 2011 30 - 39 … 44367
5 Accommodation and food service activitie… 2011 40 - 49 … 46680
6 Accommodation and food service activitie… 2011 50 - 59 … 41939
7 Accommodation and food service activitie… 2011 60 years… 35634
8 Accommodation and food service activitie… 2012 15 - 24 … 25800
9 Accommodation and food service activitie… 2012 15 years… 39347
10 Accommodation and food service activitie… 2012 25 - 29 … 37004
# ℹ 1,264 more rowsAggregated Weekly Earning Percentiles
weekly_earning_percentiles <- earning_percentiles %>%
filter(Metrics == "Weekly Earnings")
agg_weekly_earning_percentiles <- weekly_earning_percentiles %>% group_by(Calender_Year, Percentile,Age_Group ) %>%
summarise(
Total_Euros = sum(Euros, na.rm = TRUE))`summarise()` has grouped output by 'Calender_Year', 'Percentile'. You can
override using the `.groups` argument.print(agg_weekly_earning_percentiles)# A tibble: 1,729 × 4
# Groups: Calender_Year, Percentile [247]
Calender_Year Percentile Age_Group Total_Euros
<dbl> <chr> <chr> <dbl>
1 2011 10th percentile 15 - 24 years 100
2 2011 10th percentile 15 years and over 182.
3 2011 10th percentile 25 - 29 years 215.
4 2011 10th percentile 30 - 39 years 246.
5 2011 10th percentile 40 - 49 years 216.
6 2011 10th percentile 50 - 59 years 205.
7 2011 10th percentile 60 years and over 127.
8 2011 15th percentile 15 - 24 years 121.
9 2011 15th percentile 15 years and over 226.
10 2011 15th percentile 25 - 29 years 266.
# ℹ 1,719 more rowsAggregated Annual Earning Percentiles
annual_earning_percentiles <- earning_percentiles %>%
filter(Metrics == "Annual Earnings")
agg_annual_earning_percentiles <- annual_earning_percentiles %>% group_by(Calender_Year, Percentile, Age_Group) %>%
summarise(
Total_Euros = sum(Euros, na.rm = TRUE))`summarise()` has grouped output by 'Calender_Year', 'Percentile'. You can
override using the `.groups` argument.print(agg_annual_earning_percentiles)# A tibble: 1,729 × 4
# Groups: Calender_Year, Percentile [247]
Calender_Year Percentile Age_Group Total_Euros
<dbl> <chr> <chr> <dbl>
1 2011 10th percentile 15 - 24 years 6708
2 2011 10th percentile 15 years and over 12475
3 2011 10th percentile 25 - 29 years 14739
4 2011 10th percentile 30 - 39 years 16593
5 2011 10th percentile 40 - 49 years 14286
6 2011 10th percentile 50 - 59 years 12076
7 2011 10th percentile 60 years and over 7279
8 2011 15th percentile 15 - 24 years 8055
9 2011 15th percentile 15 years and over 15981
10 2011 15th percentile 25 - 29 years 17195
# ℹ 1,719 more rowsAggregated Geo Distribution
geo_distrbution <- geo_distrbution %>% group_by(County, Year) %>%
summarise(
Total_Euros = sum(Euros, na.rm = TRUE))`summarise()` has grouped output by 'County'. You can override using the
`.groups` argument.print(geo_distrbution)# A tibble: 351 × 3
# Groups: County [27]
County Year Total_Euros
<chr> <dbl> <dbl>
1 Co. Carlow 2011 936969
2 Co. Carlow 2012 926540
3 Co. Carlow 2013 932923
4 Co. Carlow 2014 935760
5 Co. Carlow 2015 948652
6 Co. Carlow 2016 947124
7 Co. Carlow 2017 973867
8 Co. Carlow 2018 994336
9 Co. Carlow 2019 1034414
10 Co. Carlow 2020 1101557
# ℹ 341 more rowsData Computation
Merge weekly and annual earning estimates (Sector-based)
earning_estimates <- full_join(weekly_earning_estimates, annual_earning_estimates,
by = c("Sector", "Calender_Year", "Age_Group"),
suffix = c("_Weekly", "_Annual"))Warning in full_join(weekly_earning_estimates, annual_earning_estimates, : Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 1 of `x` matches multiple rows in `y`.
ℹ Row 1 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.Merge weekly and annual earning percentiles (Percentile-based)
earning_percentiles <- full_join(weekly_earning_percentiles, agg_annual_earning_percentiles,
by = c("Calender_Year", "Percentile", "Age_Group"),
suffix = c("_Weekly", "_Annual"))Final merge of both tables based on Calender_Year and Age_Group
final_merged_data <- full_join(earning_estimates, earning_percentiles,
by = c("Calender_Year", "Age_Group"))Warning in full_join(earning_estimates, earning_percentiles, by = c("Calender_Year", : Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 1 of `x` matches multiple rows in `y`.
ℹ Row 1 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.head(final_merged_data) # A tibble: 6 × 14
Metrics_Weekly Calender_Year Sex_Weekly Sector Age_Group Euros_Weekly
<chr> <dbl> <chr> <chr> <chr> <dbl>
1 Mean Weekly Earnings 2011 Male Industry… 15 - 24 … 412.
2 Mean Weekly Earnings 2011 Male Industry… 15 - 24 … 412.
3 Mean Weekly Earnings 2011 Male Industry… 15 - 24 … 412.
4 Mean Weekly Earnings 2011 Male Industry… 15 - 24 … 412.
5 Mean Weekly Earnings 2011 Male Industry… 15 - 24 … 412.
6 Mean Weekly Earnings 2011 Male Industry… 15 - 24 … 412.
# ℹ 8 more variables: Metrics_Annual <chr>, Sex_Annual <chr>,
# Euros_Annual <dbl>, Metrics <chr>, Percentile <chr>, UNIT <chr>,
# Euros <dbl>, Total_Euros <dbl>Merge Geograpohical Data
counties_sf <- st_read("ie.json")Reading layer `ie' from data source
`/Users/debangshubaidya/Documents/Course Material/Semester 2/R /Assignment 2/R Project/ie.json'
using driver `GeoJSON'
Simple feature collection with 26 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -10.47818 ymin: 51.44571 xmax: -5.99352 ymax: 55.38638
Geodetic CRS: WGS 84counties_sf$name <- paste0("Co. ", counties_sf$name)
counties_data <- counties_sf %>%
left_join(geo_distrbution, by = c("name" = "County"))
head(counties_data)Simple feature collection with 6 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -8.79776 ymin: 54.46348 xmax: -6.939565 ymax: 55.38638
Geodetic CRS: WGS 84
source id name Year Total_Euros
1 https://simplemaps.com IEDL Co. Donegal 2011 808506
2 https://simplemaps.com IEDL Co. Donegal 2012 798808
3 https://simplemaps.com IEDL Co. Donegal 2013 799574
4 https://simplemaps.com IEDL Co. Donegal 2014 809065
5 https://simplemaps.com IEDL Co. Donegal 2015 824356
6 https://simplemaps.com IEDL Co. Donegal 2016 813092
geometry
1 MULTIPOLYGON (((-8.168467 5...
2 MULTIPOLYGON (((-8.168467 5...
3 MULTIPOLYGON (((-8.168467 5...
4 MULTIPOLYGON (((-8.168467 5...
5 MULTIPOLYGON (((-8.168467 5...
6 MULTIPOLYGON (((-8.168467 5...Descriptive Analysis
df <- final_merged_data
# Checking structure of the data
str(df)tibble [96,824 × 14] (S3: tbl_df/tbl/data.frame)
$ Metrics_Weekly: chr [1:96824] "Mean Weekly Earnings" "Mean Weekly Earnings" "Mean Weekly Earnings" "Mean Weekly Earnings" ...
$ Calender_Year : num [1:96824] 2011 2011 2011 2011 2011 ...
$ Sex_Weekly : chr [1:96824] "Male" "Male" "Male" "Male" ...
$ Sector : chr [1:96824] "Industry (B to E)" "Industry (B to E)" "Industry (B to E)" "Industry (B to E)" ...
$ Age_Group : chr [1:96824] "15 - 24 years" "15 - 24 years" "15 - 24 years" "15 - 24 years" ...
$ Euros_Weekly : num [1:96824] 412 412 412 412 412 ...
$ Metrics_Annual: chr [1:96824] "Mean Annual Earnings" "Mean Annual Earnings" "Mean Annual Earnings" "Mean Annual Earnings" ...
$ Sex_Annual : chr [1:96824] "Male" "Male" "Male" "Male" ...
$ Euros_Annual : num [1:96824] 24178 24178 24178 24178 24178 ...
$ Metrics : chr [1:96824] "Weekly Earnings" "Weekly Earnings" "Weekly Earnings" "Weekly Earnings" ...
$ Percentile : chr [1:96824] "5th percentile" "10th percentile" "15th percentile" "20th percentile" ...
$ UNIT : chr [1:96824] "€" "€" "€" "€" ...
$ Euros : num [1:96824] 75.3 100 121.2 141.6 161.1 ...
$ Total_Euros : num [1:96824] 5096 6708 8055 9299 10504 ...# Converting categorical variables to factors
df$Sector <- as.factor(df$Sector)
df$Age_Group <- as.factor(df$Age_Group)
df$Calender_Year <- as.integer(df$Calender_Year) # Ensure year is numeric
# Summary statistics for Total_Euros (weekly & annual)
summary(df[, c("Euros_Weekly", "Euros_Annual")]) Euros_Weekly Euros_Annual
Min. : 196.0 Min. : 12185
1st Qu.: 474.5 1st Qu.: 27418
Median : 665.0 Median : 38121
Mean : 711.2 Mean : 40372
3rd Qu.: 861.3 3rd Qu.: 49342
Max. :2264.4 Max. :119689 # More detailed stats
describe(df[, c("Euros_Weekly", "Euros_Annual")]) vars n mean sd median trimmed mad min
Euros_Weekly 1 96824 711.23 316.36 665.02 677.55 285.82 195.97
Euros_Annual 2 96824 40372.30 16984.60 38121.00 38749.88 16170.72 12185.00
max range skew kurtosis se
Euros_Weekly 2264.39 2068.42 1.25 2.36 1.02
Euros_Annual 119689.00 107504.00 1.10 1.88 54.58# Standard deviation & variance
sd(df$Euros_Weekly, na.rm = TRUE)[1] 316.3643var(df$Euros_Weekly, na.rm = TRUE)[1] 100086.4sd(df$Euros_Annual, na.rm = TRUE)[1] 16984.6var(df$Euros_Annual, na.rm = TRUE)[1] 288476527# Boxplot for Weekly vs Annual Earnings
# Weekly Earnings Distribution
fig_weekly <- plot_ly(df,
x = ~Age_Group,
y = ~Euros_Weekly,
type = "box",
boxmean = "sd",
marker = list(color = 'rgba(0, 123, 255, 0.7)', # Transparent blue
line = list(color = 'rgba(0, 123, 255, 1)', width = 2)),
fillcolor = 'rgba(0, 123, 255, 0.4)',
jitter = 0.3) %>%
layout(title = "Weekly Earnings Distribution by Age Group",
title_x = 0.5,
title_y = 1.1,
xaxis = list(title = "Age Group",
tickangle = 45,
titlefont = list(size = 14)),
yaxis = list(title = "Total Euros (Weekly)",
titlefont = list(size = 14)),
plot_bgcolor = 'rgba(245, 245, 245, 1)', # Light gray background
paper_bgcolor = 'rgba(255, 255, 255, 1)', # White paper background
showlegend = FALSE)
fig_weekly # Display the plotWarning: 'layout' objects don't have these attributes: 'title_x', 'title_y'
Valid attributes include:
'_deprecated', 'activeshape', 'annotations', 'autosize', 'autotypenumbers', 'calendar', 'clickmode', 'coloraxis', 'colorscale', 'colorway', 'computed', 'datarevision', 'dragmode', 'editrevision', 'editType', 'font', 'geo', 'grid', 'height', 'hidesources', 'hoverdistance', 'hoverlabel', 'hovermode', 'images', 'legend', 'mapbox', 'margin', 'meta', 'metasrc', 'modebar', 'newshape', 'paper_bgcolor', 'plot_bgcolor', 'polar', 'scene', 'selectdirection', 'selectionrevision', 'separators', 'shapes', 'showlegend', 'sliders', 'smith', 'spikedistance', 'template', 'ternary', 'title', 'transition', 'uirevision', 'uniformtext', 'updatemenus', 'width', 'xaxis', 'yaxis', 'barmode', 'bargap', 'mapType'This boxplot visualizes the distribution of weekly earnings across different age groups, revealing several key insights. Younger workers (15-24 years) exhibit the lowest median earnings, with relatively tight interquartile ranges (IQR), indicating less variation in earnings. As age increases, earnings generally rise, with the 40-49 and 50-59 age groups showing the highest median weekly earnings. However, earnings dispersion also increases with age, as evidenced by the wider IQRs and more frequent outliers in older groups, suggesting greater wage inequality among more experienced workers. The presence of many outliers, especially in older age groups, indicates that some individuals earn significantly above the typical range, likely due to senior positions, high-skilled professions, or bonuses. Interestingly, the 60+ age group sees a decline in median earnings compared to the peak earning years, potentially due to retirement transitions or part-time employment. This distribution highlights the expected career wage trajectory, where earnings grow with experience before tapering off later in life.
# Annual Earnings Distribution
fig_annual <- plot_ly(df,
x = ~Age_Group,
y = ~Euros_Annual,
type = "box",
boxmean = "sd",
marker = list(color = 'rgba(255, 99, 132, 0.7)', # Transparent red
line = list(color = 'rgba(255, 99, 132, 1)', width = 2)),
fillcolor = 'rgba(255, 99, 132, 0.4)',
jitter = 0.3) %>%
layout(title = "Annual Earnings Distribution by Age Group",
title_x = 0.5,
title_y = 1.1,
xaxis = list(title = "Age Group",
tickangle = 45,
titlefont = list(size = 14)),
yaxis = list(title = "Total Euros (Annual)",
titlefont = list(size = 14)),
plot_bgcolor = 'rgba(245, 245, 245, 1)', # Light gray background
paper_bgcolor = 'rgba(255, 255, 255, 1)', # White paper background
showlegend = FALSE)
fig_annual # Display the plotWarning: 'layout' objects don't have these attributes: 'title_x', 'title_y'
Valid attributes include:
'_deprecated', 'activeshape', 'annotations', 'autosize', 'autotypenumbers', 'calendar', 'clickmode', 'coloraxis', 'colorscale', 'colorway', 'computed', 'datarevision', 'dragmode', 'editrevision', 'editType', 'font', 'geo', 'grid', 'height', 'hidesources', 'hoverdistance', 'hoverlabel', 'hovermode', 'images', 'legend', 'mapbox', 'margin', 'meta', 'metasrc', 'modebar', 'newshape', 'paper_bgcolor', 'plot_bgcolor', 'polar', 'scene', 'selectdirection', 'selectionrevision', 'separators', 'shapes', 'showlegend', 'sliders', 'smith', 'spikedistance', 'template', 'ternary', 'title', 'transition', 'uirevision', 'uniformtext', 'updatemenus', 'width', 'xaxis', 'yaxis', 'barmode', 'bargap', 'mapType'This boxplot of annual earnings across age groups highlights the expected income trajectory over a working lifetime. The median earnings increase steadily from younger age groups, peaking around the 40-49 and 50-59 age groups before slightly declining in the 60+ category. The widening interquartile range (IQR) as age increases suggests that income disparity grows with experience, likely due to differences in career progression, job roles, and seniority levels. Notably, the presence of numerous high-earning outliers, especially in the older age groups, reflects the impact of bonuses, executive positions, and specialized skills commanding higher salaries. The decline in median earnings for the 60+ group may be attributed to retirement transitions, reduced working hours, or a shift toward lower-paying roles post-retirement. Overall, the distribution underscores the economic benefits of experience and tenure while also showcasing increasing wage inequality among older professionals.
# Checking correlation between numeric variables
cor(df[, c("Euros_Weekly", "Euros_Annual")], use = "complete.obs") Euros_Weekly Euros_Annual
Euros_Weekly 1.0000000 0.8046444
Euros_Annual 0.8046444 1.0000000# Interactive- Bar Chart - Earnings Trend Over Time
# Compute mean annual earnings per year
trend_data <- df %>%
group_by(Calender_Year) %>%
summarise(Avg_Annual_Earnings = mean(Euros_Annual, na.rm = TRUE))
# Interactive line chart
p1 <- plot_ly(trend_data, x = ~Calender_Year,
y = ~Avg_Annual_Earnings,
type = "scatter", mode = "lines+markers",
name = "Annual Earnings",
line = list(color = "blue")) %>%
layout(title = "Earnings Trend Over the Years",
xaxis = list(title = "Year"),
yaxis = list(title = "Earnings (€)"))
p1 # Display the plotThe earnings trend in Ireland over the years exhibits a significant upward trajectory, particularly from 2015 onward, which aligns with the country’s post-recession economic recovery. Between 2011 and 2014, wages remained relatively stagnant, reflecting the lingering effects of the global financial crisis and Ireland’s subsequent austerity measures under the EU-IMF bailout program. However, from 2015 onward, the sharp rise in earnings coincides with Ireland’s economic boom, fueled by a surge in foreign direct investment (FDI), particularly in the tech and pharmaceutical sectors. The steep increase post-2019 could be linked to labor market tightening, minimum wage increases, and inflationary pressures, further exacerbated by the COVID-19 pandemic, which led to wage subsidies and support schemes that influenced overall earnings. Additionally, the rapid rise in salaries after 2020 reflects Ireland’s strong post-pandemic recovery, driven by multinational corporations and a resilient domestic economy. This trend underscores Ireland’s shift toward a high-income economy, albeit with concerns over cost-of-living increases and wage disparity across different sectors.
# Interactive Heatmap - Earnings Percentile Distribution
df_filtered <- df %>%
filter(Percentile != "5th percentile")
# Heatmap
p2 <- plot_ly(df_filtered, x = ~Calender_Year, y = ~Percentile, z = ~Total_Euros,
type = "heatmap", colorscale = "Blues") %>%
layout(title = "Heatmap of Annual Earnings Percentile Distribution",
xaxis = list(title = "Year"),
yaxis = list(title = "Percentile"),
colorbar = list(title = "Earnings (€)"))
p2 # Display the plotWarning: 'layout' objects don't have these attributes: 'colorbar'
Valid attributes include:
'_deprecated', 'activeshape', 'annotations', 'autosize', 'autotypenumbers', 'calendar', 'clickmode', 'coloraxis', 'colorscale', 'colorway', 'computed', 'datarevision', 'dragmode', 'editrevision', 'editType', 'font', 'geo', 'grid', 'height', 'hidesources', 'hoverdistance', 'hoverlabel', 'hovermode', 'images', 'legend', 'mapbox', 'margin', 'meta', 'metasrc', 'modebar', 'newshape', 'paper_bgcolor', 'plot_bgcolor', 'polar', 'scene', 'selectdirection', 'selectionrevision', 'separators', 'shapes', 'showlegend', 'sliders', 'smith', 'spikedistance', 'template', 'ternary', 'title', 'transition', 'uirevision', 'uniformtext', 'updatemenus', 'width', 'xaxis', 'yaxis', 'barmode', 'bargap', 'mapType'The heatmap illustrates the distribution of annual earnings percentiles over time, showing a clear upward trend, especially in the higher percentiles (e.g., the 95th and 90th percentiles). This suggests that high earners in Ireland have seen significant wage growth, while lower percentiles have experienced relatively stagnant wage progression. This aligns with Ireland’s evolving labor market dynamics, where high-skilled and tech-sector jobs have surged, benefiting top earners. Meanwhile, wage growth for lower-income workers has been slower, possibly due to factors such as automation, labor outsourcing, and a rise in gig economy employment. The widening gap also reflects broader global trends of wage polarization, where knowledge-intensive industries offer higher rewards while traditional sectors struggle with wage stagnation.
Inferential Analysis
# GLM model: Predicting Annual Earnings based on Weekly Earnings, Age Group, and Sector
glm_model <- glm(Euros_Annual ~ Euros_Weekly + Age_Group + Sector,
data = df, family = gaussian())
# GAM model: Adding smoothing function for Weekly Earnings
gam_model <- gam(Euros_Annual ~ s(Euros_Weekly) + Age_Group + Sector,
data = df, family = gaussian())
# Plotting GAM effect
plot(gam_model, pages = 1)
The Generalized Additive Model (GAM) plot illustrates the smooth relationship between weekly earnings (Euros_Weekly) and its modeled effect. The curve suggests a nonlinear but generally increasing relationship, with a steeper slope at higher income levels, indicating accelerating gains for higher earners. The confidence bands (dashed lines) remain relatively narrow, suggesting strong model confidence across most of the range. However, at the lower end, the confidence bands widen slightly, possibly due to greater variance in earnings for lower-wage workers. This pattern aligns with Ireland’s labor market trends, where wage growth disproportionately benefits higher-income brackets, particularly in tech and finance, while lower-income workers see more modest increases, reflecting potential wage polarization
# GAM model: Adding smoothing function for Weekly Earnings
gam_model <- gam(Euros_Annual ~ s(Euros_Weekly) + Age_Group + Sector,
data = df, family = gaussian())
# Plot GAM effect
plot(gam_model, pages = 1)
# Checking Residuals
par(mfrow = c(2, 2)) # Multiple plots
plot(glm_model)
The Residuals vs Fitted plot suggests potential heteroscedasticity, as residuals exhibit increasing spread at higher predicted values, indicating that variance grows with earnings. The Q-Q Residuals plot reveals right-skewed residuals, with extreme values deviating from the normal distribution, suggesting possible outliers among high earners. The Scale-Location plot further supports heteroscedasticity concerns, as the red trend line slopes upward, highlighting non-constant variance. Additionally, the Residuals vs Leverage plot identifies a few high-leverage points, particularly the observation labeled “87489,” which may exert undue influence on the model. These diagnostics suggest that while the GAM effectively captures nonlinear trends,
# Check R-squared for GAM
rsq <- 1 - sum(resid(gam_model)^2) / sum((df$Euros_Annual - mean(df$Euros_Annual, na.rm = TRUE))^2)
print(paste("R-squared of GAM model:", round(rsq, 3)))[1] "R-squared of GAM model: 0.718"The R-squared value of 0.718 means that the GAM explains about 71.8% of the variation in annual earnings. This shows that the model does a good job of capturing important patterns in the data, especially the non-linear relationships. However, 28.2% of the variation is still unexplained, which suggests that other factors like job type, education, or economic conditions might also affect earnings. Since the diagnostic plots showed some issues like unequal variance and possible outliers, improving the model by adding more variables or adjusting for these issues could make it even more accurate.
Geographical Analysis
# Create a color palette for the map
pal <- colorNumeric(palette = "YlGnBu", domain = counties_data$Total_Euros)
# Filter data for two specific years (2011 and 2021)
year_data_one <- counties_data %>%
filter(Year == 2011)
year_data_two <- counties_data %>%
filter(Year == 2021)
# Create color palette for Total Euros
pal <- colorNumeric(palette = "YlGnBu", domain = counties_data$Total_Euros)
# Create map for 2011
map_one <- leaflet(data = year_data_one) %>%
addTiles() %>%
addPolygons(fillColor = ~pal(Total_Euros),
fillOpacity = 0.7,
color = "white",
weight = 1,
popup = ~paste(name, "<br>", "Total Earnings: €", Total_Euros),
label = ~name) %>%
addLegend(pal = pal, values = ~Total_Euros, opacity = 0.7, title = "Total Earnings (€)", position = "bottomright")
# Create map for 2021
map_two <- leaflet(data = year_data_two) %>%
addTiles() %>%
addPolygons(fillColor = ~pal(Total_Euros),
fillOpacity = 0.7,
color = "white",
weight = 1,
popup = ~paste(name, "<br>", "Total Earnings: €", Total_Euros),
label = ~name) %>%
addLegend(pal = pal, values = ~Total_Euros, opacity = 0.7, title = "Total Earnings (€)", position = "bottomright")
# Combine both maps side by side
browsable(tagList(
div(style = "display: inline-block; width: 48%;", map_one),
div(style = "display: inline-block; width: 48%;", map_two)
))Between 2011 and 2021, Ireland saw a significant increase in total industrial earnings across its counties, as depicted in the two maps. In 2011 (left image), earnings were relatively lower, with most counties displaying lighter shades of green, signifying lower total earnings. By 2021 (right image), the colors have transitioned to darker shades of blue, indicating a marked rise in earnings across all regions.
This growth in industrial earnings correlates strongly with Ireland’s economic and industrial developments over the decade. Key events that influenced this trend include:
Economic Recovery Post-2008 Crisis
In 2011, Ireland was still recovering from the financial crisis, and industrial growth was sluggish. Government austerity measures were in place, and investment in key industries was still regaining momentum. This is reflected in the lower earnings in the 2011 map.
Growth of the Technology and Pharmaceutical Sectors
From 2011 to 2021, Ireland became a hub for multinational corporations, particularly in technology and pharmaceuticals. Companies like Apple, Google, Facebook, and Pfizer significantly expanded their operations. This influx of investment contributed to rising wages and industrial earnings, particularly in counties with strong industrial and tech presence.
Brexit (2016-2020) and Its Impact
The uncertainty and eventual execution of Brexit led to changes in trade dynamics, with some industries in Ireland benefiting from companies relocating operations from the UK. Certain counties with strong manufacturing and logistics sectors likely saw increased earnings due to these shifts.
COVID-19 and Industrial Resilience (2020-2021)
The pandemic had mixed effects on industries, but Ireland’s pharmaceutical and tech industries remained robust, even experiencing growth due to increased global demand. The rise in earnings in 2021 suggests that certain industrial sectors thrived despite economic challenges.
Gender Pay Gap Analysis
# Load and prepare data
gender_data <- read_csv("DDA02.20250317T140346.csv") %>%
filter(Sex %in% c("Male", "Female")) %>%
rename(Metrics = `Statistic Label`,
Calender_Year = Year,
Sector = `NACE Rev 2 Sector`,
Age_Group = `Age Group`,
Euros = VALUE)Rows: 30576 Columns: 12
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): STATISTIC, Statistic Label, C02199V02655, Sex, C02665V03225, NACE R...
dbl (4): TLIST(A1), Year, C02076V02508, VALUE
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Calculate metrics
gender_gap_enhanced <- gender_data %>%
filter(Metrics == "Mean Annual Earnings") %>%
group_by(Calender_Year, Sector) %>%
summarise(
Male_Earnings = mean(Euros[Sex == "Male"], na.rm = TRUE),
Female_Earnings = mean(Euros[Sex == "Female"], na.rm = TRUE),
Pay_Gap = (Male_Earnings - Female_Earnings) / Male_Earnings * 100,
Ratio = Female_Earnings / Male_Earnings,
Absolute_Gap = Male_Earnings - Female_Earnings,
.groups = 'drop'
)
# Visualization
p <- gender_gap_enhanced %>%
plot_ly() %>%
add_trace(
x = ~Calender_Year,
y = ~Pay_Gap,
color = ~Sector,
type = 'scatter',
mode = 'lines+markers',
line = list(width = 3),
marker = list(size = 10),
text = ~paste0(
"<b>", Sector, "</b><br>",
"Year: ", Calender_Year, "<br>",
"Pay Gap: ", round(Pay_Gap, 1), "%<br>",
"Male: €", format(round(Male_Earnings), big.mark = ","), "<br>",
"Female: €", format(round(Female_Earnings), big.mark = ",")),
hoverinfo = 'text'
) %>%
layout(
title = list(
text = "<b>Gender Pay Gap by Economic Sector</b>",
x = 0.05,
font = list(size = 20)
),
xaxis = list(
title = "Year",
dtick = 1,
gridcolor = '#f0f0f0',
showgrid = TRUE
),
yaxis = list(
title = "Pay Gap (%)",
ticksuffix = "%",
range = c(
min(gender_gap_enhanced$Pay_Gap, na.rm = TRUE) * 1.1,
max(gender_gap_enhanced$Pay_Gap, na.rm = TRUE) * 1.1
),
gridcolor = '#f0f0f0',
zerolinecolor = '#333',
zerolinewidth = 2
),
legend = list(
orientation = 'h',
x = 0.5,
xanchor = 'center',
y = -0.4, # Slightly lower to provide more space
font = list(size = 12)
),
margin = list(t = 80, b = 150), # Increased bottom margin
plot_bgcolor = 'rgba(0,0,0,0)',
paper_bgcolor = 'rgba(0,0,0,0)'
)
# For Quarto, increase figure height
#| echo: false
#| warning: false
#| message: false
#| layout: l-body-outset
#| fig-width: 16
#| fig-height: 18 # Increased from 14 to 18
#| fig-align: center
# Explicitly print the plot for Quarto
pWarning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors